Search Results (223)

Subway systems are highly vulnerable to disasters because of their confined underground structures and limited evacuation routes, making accurate evacuation analysis essential for reducing casualties. Most existing studies overlook panic behavior, leading to unrealistic assessments of evacuation efficiency. This study develops a modeling framework that integrates panic behavior into agent-based subway evacuation simulations. The framework incorporates three behavioral factors: reaction delays to emergency cues, hesitation at decision points, and irrational route choices. Simulation experiments were conducted under different occupancy conditions, and results from panic-integrated and non-panic scenarios were compared. Findings show that the inclusion of panic significantly prolongs evacuation time, with delays of up to 30% in full-scale scenarios due to congestion and route errors. These outcomes demonstrate that panic behavior exerts a decisive influence on evacuation dynamics and should not be neglected in simulation studies. Incorporating panic into evacuation modeling provides a more realistic basis for designing safer subway systems and developing effective emergency response strategies. Full article

Background: CP (CP) and HCP (HCP) are rare and high-risk conditions, often historically managed with radical intervention and associated with hemorrhage and fertility loss. Objective: To summarize current evidence on the conservative, fertility-preserving management of cervical and heterotopic cervical pregnancies and to illustrate a stepwise pharmacologic protocol applied in our tertiary center. Methods: A narrative literature review (PubMed, Scopus, Web of Science; inception—July 2025) was conducted using the following key terms: “CP,” “HCP,” “methotrexate,” “mifepristone,” “misoprostol,” “uterine artery embolization,” “hysteroscopy,” and “Doppler ultrasound.” We integrated a personal institutional case that applied stepwise pharmacologic priming, Doppler-guided surveillance, and delayed evacuation. Results: Evidence—primarily from case reports and small series—supports conservative, multi-modal strategies combining systemic or local methotrexate ± mifepristone, timed to Doppler-confirmed vascular regression, before surgical intervention. Adjuncts such as misoprostol, hysteroscopic resection, balloon tamponade, and uterine artery embolization further reduce hemorrhage risk while maintaining fertility. Our case utilized a novel, incremental dosing strategy of mifepristone followed by methotrexate, a week-long interval to confirm vascular involution via Doppler, and delayed suction curettage with minimal blood loss. Conclusions: Conservative, imaging-guided management is promising for reducing hemorrhagic complications and preserving fertility in CP/HCP. Future multicenter registries and standardized Doppler-based protocols are urgently needed to refine decision-making and optimize outcomes. Full article

Given the limitation of existing slope collapse monitoring technology, which relies on surface sensors, and the difficulty in capturing the precursors of deep rock and soil instability, this study used rock anchor embedded sensing technology to conduct collapse tests on artificial simulated slopes. Two groups of control conditions were designed: (1) shotcrete reinforced slope and natural slope; and (2) GFRP anchor and spiral steel anchor support system. The deformation characteristics of the slope at the initial stage of collapse were analyzed. The results show that the monitoring method based on the stress–strain response of deep rock mass significantly improved the early warning effect. GFRP anchor had a lower elastic modulus and responded more sensitively to small displacements than spiral steel anchor. Shotcrete reinforcement transformed slope deformation from ‘local dispersed deformation’ to ‘overall coordinated deformation’ and delayed slope instability via the ‘deformation hysteresis effect’. This study provides key technical parameters for the intelligent monitoring system of high-risk slopes as well as support for pre-disaster emergency evacuation decision-making and the establishment of intelligent early warning systems. Full article

Although liquefied natural gas (LNG) is already widely used as a marine fuel, its use on large cruise ships is a relatively new development. By the end of 2024, twenty-four LNG-fuelled cruise ships were in operation, each carrying several thousand passengers and making frequent port calls. These operational characteristics increase the potential risks compared to conventional cargo ships and require a rigorous safety assessment. In this study, the safety of LNG-fuelled cruise ships is assessed using the Formal Safety Assessment (FSA) framework prescribed by the International Maritime Organization (IMO). The assessment includes a hazard identification (HAZID), a risk analysis, an evaluation of risk control options, a cost–benefit analysis and recommendations for decision-making. Given the limited operational data on LNG-fuelled cruise ships, event trees are developed on the basis of LNG tanker incidents, adjusted to reflect passenger-related risks and cruise-specific operating conditions. A statistical overview of marine casualties involving cruise ships and LNG carriers of more than 20,000 GT over the last 35 years provides a further basis for the analysis. To ensure compliance, the study also analyses class requirements and regulatory frameworks, including risk assessments for ship design, bunker operations and emergency preparedness. These assessments, which are carried out at component, ship and process level, remain essential for safety validation and regulatory approval. The results provide a comprehensive framework for assessing LNG safety in the cruise sector by combining existing safety data, regulatory standards and probabilistic risk modelling. Recent work also confirms that event tree modelling identifies critical accident escalation pathways, particularly in scenarios involving passenger evacuation and port operations, which are under-researched in current practice. The results contribute to the wider debate on alternative fuels and support evidence-based decision-making by ship operators, regulators and industry stakeholders. Full article

Objectives: To evaluate the potential associations of the type of complete hydatidiform mole (CHM) initial treatment (hysterectomy or uterine evacuation) with GTN development, need for chemotherapy, and treatment outcome in women aged ≥ 40 years. Methods: This multicentric retrospective cohort study included women ≥ 40 years with CHM, initially treated between 1990 and 2018, at four different centers. Data collected included patient demographics and clinical characteristics. The outcome variables were post-CHM GTN development, need for chemotherapy for hCG normalization, surgical complications, and time to remission. Univariate and multivariate analyses were performed using chi-square, Mann–Whitney, Fisher’s exact tests, and Poisson regression. Results: 275 women with CHM aged ≥ 40 years were included in the analysis. Median patient age was significantly higher among hysterectomy patients (47 × 44 years, p = 0.01). Multivariate analysis showed that compared with uterine evacuation (244/275, 89%), hysterectomy (31/275, 11%) was associated with an 83% lower risk of GTN [RR = 0.17 95% CI = (0.04–0.71); p = 0.015] and a 92% lower risk of requiring chemotherapy [RR: 0.08 (0.01–0.64), p = 0.016]. Median time to hCG normalization did not statistically differ between treatments. No significant differences were observed between hysterectomy and uterine evacuation in terms of FIGO staging (p = 0.221) or prognostic risk score (p = 0.576). Resistance to first-line chemotherapy (17/72; 23.6%) and relapse (3/72; 4.1%) were observed only in patients undergoing initial uterine evacuation. Hysterectomy complications occurred in 45.1% (14) of the patients. Conclusions: CHM initial treatment with hysterectomy was associated with a lower risk for GTN occurrence and need for chemotherapy in women aged 40 years or older. However, shared decision-making about surgery should be tailored to each patient and their risk factors and preferences. Further, larger controlled studies are required to support our findings. Full article

This paper explores the potential of Electric Vehicle (EV) Vehicle-to-Home (V2H) technology to enhance household resilience during extreme weather events, integrating socio-economic, technical, and human rights perspectives. V2H technology enables EVs to provide backup power during outages, offering a promising solution for disaster preparedness and response. However, widespread adoption of this technology faces barriers shaped by socio-economic disparities, including income, housing, education, and access to infrastructure, as well as human decisions related to EV ownership, V2H utilization, and evacuation behaviors. To investigate these challenges, this study adopts a qualitative review of existing literature and policy frameworks, critically analyzing how social vulnerabilities and adoption barriers influence the effectiveness of V2H in improving household-level disaster resilience. The findings indicate that while V2H technology can significantly support disaster resilience, its benefits are contingent on equitable access, affordability, and public awareness. To maximize its potential, various public and private stakeholders must adopt equity-driven strategies that align technological innovation with socio-economic inclusion. This paper highlights the need for cross-sector collaboration to ensure V2H systems reach underserved and marginalized communities, advocating for policies that prioritize both technological advancement and distributive justice. Full article

This research considers the production of Potassium Nitrate product, a water-soluble nitrogen–potassium (NK) fertilizer containing 13.7% nitrogen and 46% potassium oxide. Potassium Nitrate (NOP) is produced as a fertilizer grade. The current system incurred high energy consumption, elevated emissions of greenhouse gases, resource degradation, and excessive production costs. Consequently, this research aims to implement the four steps of Cleaner Production (CP) to assess the environmental impacts of Potassium Nitrate products and their main manufacturing processes, and identify the best solution that achieves environmental goals. Environmental assessment was then used to calculate the unit indicators for raw materials, energy, waste generation, product, and packaging. The results showed that the integrated indicator was 5.18, with the energy profile being the most influential factor. Solar thermal and photovoltaic (PV) cell systems were suggested to reduce the high consumption of heavy fuel oil (HFO), including a solar thermal system to support the steam boilers and photovoltaic cells to support the electrical generator. The two alternatives were assessed based on multiple criteria using feasibility analysis and the Analytical Hierarchical Process (AHP). The solar thermal system, comprising 250 evacuated tube collectors, was preferable and resulted in savings of HFO by 121 tons/year, which led to a reduction in gaseous emissions by 375.6 metric tons of CO₂ and 21.685 kg of N₂O per year. Such improvements can also result in significant cost reductions. In conclusion, applying the CP methodology supported decision-makers in deciding the best system to enhance energy efficiency and reduce environmental nuisance at NOP plants. Full article

In the southeast of China, seasonal rainfall intensity is high, the distribution of mountains and hills is extensive, and many small-scale, shallow landslides frequently occur after consecutive seasons of heavy rainfall. High-precision automated identification systems can quickly pinpoint the scope of the disaster and help with important decisions like evacuating people, managing engineering, and assessing damage. Many people have designed systems for detecting such shallow landslides, but few have designed systems that combine high resolution, high automation, and real-time capability of landslide identification. Taking accuracy, automation, and real-time capability into account, we designed an automatic rainfall-induced shallow landslide detection system based on deep learning and Unmanned Aerial Vehicle (UAV) images. The system uses UAVs to capture high-resolution imagery, the U-Net (a U-shaped convolutional neural network) to combine multi-scale features, an adaptive edge enhancement loss function to improve landslide boundary identification, and the development of the “UAV Cruise Geological Hazard AI Identification System” software with an automated processing chain. The system integrates UAV-specific preprocessing and achieves a processing speed of 30 s per square kilometer. It was validated in Wanli District, Nanchang City, Jiangxi Province. The results show a Mean Intersection over Union (MIoU) of 90.7% and a Pixel Accuracy of 92.3%. Compared with traditional methods, the system significantly improves the accuracy of landslide detection. Full article

Road networks are essential for access, resource distribution, and population evacuation during natural events. These challenges are pronounced in rural areas, where network redundancy is limited and communities may have social disparities. While traditional risk management systems often focus on the physical consequences of hazard events alone, specialized literature increasingly suggests the development of a more comprehensive approach for risk assessment, where not only physical aspects associated with infrastructure, such as damage level or disruptions, but also the social and economic attributes of the affected population are considered. Consequently, this paper proposes a Vulnerability Access Index (VAI) to support road network decision-making that integrates the social vulnerability of rural communities exposed to natural events, their accessibility to nearby critical infrastructure, and physical risk. The research methodology considers (i) the Social Vulnerability Index (SVI) calculation based on socioeconomic variables, (ii) Importance Index estimation (I_imp) to evaluate access to critical infrastructure, (iii) VAI calculation combining SVI and I_imp, and (iv) application to a case study in the influence area of the Villarrica volcano in southern Chile. The results show that when incorporating social variables and accessibility, infrastructure criticality varies significantly compared to the infrastructure criticality assessment based solely on physical risk, modifying the decision-making regarding road infrastructure robustness and resilience improvements. Full article

Background: Intracerebral hemorrhage (ICH) is a severe form of stroke associated with high morbidity and mortality. While neurosurgical evacuation may offer theoretical benefits, its impact on survival and hospital course remains debated. We aimed to compare the outcomes of surgical versus conservative management in patients with lobar, capsulo-lenticular, and thalamic ICH and to identify factors influencing mortality and the surgical decision. Methods: This single-center, retrospective cohort study included adult patients admitted to the County Clinical Emergency Hospital of Sibiu (2017–2023) with spontaneous supratentorial ICH confirmed via CT (deepest affected structure determining lobar, capsulo-lenticular, or thalamic location). We collected data on demographics, clinical presentation (Glasgow Coma Scale [GCS], anticoagulant use), hematoma characteristics (volume, extension), treatment modality (surgical vs. conservative), and in-hospital outcomes (mortality, length of stay). Statistical analyses included t-tests, χ², correlation tests, and logistic regression to identify independent predictors of mortality and surgery. Results: A total of 445 patients were analyzed: 144 lobar, 150 capsulo-lenticular, and 151 thalamic. Surgical intervention was more common in patients with larger volumes and lower GCS. Overall, in-hospital mortality varied by location, reaching 13% in the lobar group, 20.7% in the capsulo-lenticular group, and 35.1% in the thalamic group. Within each location, surgical intervention did not significantly reduce overall in-hospital mortality despite the more severe baseline presentation in surgical patients. In lobar ICH specifically, no clear survival advantage emerged, although surgery may still benefit those most severely compromised. For capsulo-lenticular hematomas > 30 mL, surgery was associated with lower mortality (39.4% vs. 61.5%). In patients with large lobar ICH, surgical intervention was associated with mortality rates similar to those seen in less severe, conservatively managed cohorts. Multivariable adjustment confirmed GCS and hematoma volume as independent mortality predictors; age and volume predicted the likelihood of surgical intervention. Conclusions: Despite targeting more severe cases, neurosurgical evacuation did not uniformly lower in-hospital mortality. In lobar ICH, surgical patients with larger hematomas (~48 mL) and lower GCS (~11.6) had mortality rates (~13%) comparable to less severe, conservative cohorts, indicating that surgical intervention was associated with similar mortality rates despite higher baseline risk. However, these findings do not establish a causal survival benefit and should be interpreted in the context of non-randomized patient selection. For capsulo-lenticular hematomas > 30 mL, surgery was associated with lower observed mortality (39.4% vs. 61.5%). Thalamic ICH remained most lethal, highlighting the difficulty of deep-brain bleeds and frequent ventricular extension. Across locations, hematoma volume and GCS were the primary outcome predictors, indicating the need for timely intervention, better patient selection, and possibly minimally invasive approaches. Future prospective multicenter research is necessary to refine surgical indications and validate these findings. To our knowledge, this investigation represents the largest and most contemporary single-center cohort study of supratentorial intracerebral hemorrhage conducted in Romania. Full article

Emergencies can occur unexpectedly and require immediate action, especially in aviation, where time pressure and uncertainty are high. This study focused on improving emergency evacuation in airport and aircraft scenarios using real-time decision-making support. A system based on the Asynchronous Advantage Actor–Critic (A3C) algorithm, an advanced deep reinforcement learning method, was developed to generate faster and more efficient evacuation routes compared to traditional models. The A3C model was tested in various scenarios, including different environmental conditions and numbers of agents, and its performance was compared with the Deep Q-Network (DQN) algorithm. The results showed that A3C achieved evacuations 43.86% faster on average and converged in fewer episodes (100 vs. 250 for DQN). In dynamic environments with moving threats, A3C also outperformed DQN in maintaining agent safety and adapting routes in real time. As the number of agents increased, A3C maintained high levels of efficiency and robustness. These findings demonstrate A3C’s strong potential to enhance evacuation planning through improved speed, adaptability, and scalability. The study concludes by highlighting the practical benefits of applying such models in real-world emergency response systems, including significantly faster evacuation times, real-time adaptability to evolving threats, and enhanced scalability for managing large crowds in high-density environments including airport terminals. The A3C-based model offers a cost-effective alternative to full-scale evacuation drills by enabling virtual scenario testing, supports proactive safety planning through predictive modeling, and contributes to the development of intelligent decision-support tools that improve coordination and reduce response time during emergencies. Full article

Assessing building fire safety risks during the early design phase is vital for developing practical solutions to minimize loss of life and property. This study aims to identify research trends and provide a guiding framework for researchers by systematically reviewing the literature on integrating machine learning-based predictive methods into building fire safety design using bibliometric methods. This study evaluates machine learning applications in fire safety using a comprehensive approach that combines bibliometric and content analysis methods. For this purpose, as a result of the scan without any year limitation from the Web of Science Core Collection-Citation database, 250 publications, the first of which was published in 2001, and the number has increased since 2019, were reached, and sample analysis was performed. In order to evaluate the contribution of qualified publications to science more accurately, citation counts were analyzed using normalized citation counts that balanced differences in publication fields and publication years. Multiple regression analysis was applied to support this metric’s theoretical basis and determine the impact levels of variables affecting the metric’s value (such as total citation count, publication year, and number of articles). Thus, the statistical impact of factors influencing the formation of the normalized citation count was measured, and the validity of the approach used was tested. The research categories included evacuation and emergency management, fire detection, and early warning systems, fire dynamics and spread prediction, fire load, and material risk analysis, intelligent systems and cyber security, fire prediction, and risk assessment. Convolutional neural networks, artificial neural networks, support vector machines, deep neural networks, you only look once, deep learning, and decision trees were prominent as machine learning categories. As a result, detailed literature was presented to define the academic publication profile of the research area, determine research fronts, detect emerging trends, and reveal sub-themes. Full article

Background and Clinical Significance: Local anesthetic injections, routine in dental practice, ensure pain control during procedures like root canal treatments. Though generally safe, they can occasionally cause hematomas, localized blood accumulations in tissue planes. Rapidly expanding hematomas in the head and neck are exceptionally rare but dangerous due to anatomical complexity, potentially threatening the airway. This case report emphasizes the critical need for the prompt recognition and management of such complications to prevent life-threatening outcomes, highlighting vigilance in routine dental procedures. Case Presentation: A 63-year-old male presented with rapidly enlarging right buccal swelling four hours post-local anesthetic injection for a root canal on a right maxillary molar. Examination showed warm, erythematous edema and buccal ecchymosis; a CT scan confirmed a 3.8 cm × 8.4 cm × 5.5 cm buccal space hematoma. His medical history revealed controlled type 2 diabetes and hyperlipidemia, and his coagulation was normal. Conservative management failed as the hematoma progressed, limiting mouth and eye opening. Urgent surgical decompression under general anesthesia evacuated clots and ligated facial and angular arteries. ICU monitoring ensured airway stability, with discharge on day three with antibiotics and follow-up. Conclusions: This case highlights the rare potential for dental anesthetic injections to cause rapidly progressive hematomas, requiring urgent surgical intervention and multidisciplinary care to prevent airway compromise. Early recognition, imaging, and decisive management are vital in achieving favorable outcomes in such serious complications. Full article

Fire, whether wild or urban, depends on the triad of oxygen, fuel, and heat. Urban fires, although smaller in scale, have devastating impacts, as evidenced by the 2018 wildfire in Mati, Attica (Greece), which claimed 104 lives. The elderly and children are the most vulnerable due to mobility and cognitive limitations. This study applies Grammatical Evolution (GE), a machine learning method that generates interpretable classification rules to predict the consequences of urban fires. Using historical data (casualties, containment time, and meteorological/demographic parameters), GE produces classification rules in human-readable form. The rules achieve over 85% accuracy, revealing critical correlations. For example, high temperatures (>35 °C) combined with irregular building layouts exponentially increase fatality risks, while firefighter response time proves more critical than fire intensity itself. Applications include dynamic evacuation strategies (real-time adaptation), preventive urban planning (fire-resistant materials and green buffer zones), and targeted awareness campaigns for at-risk groups. Unlike “black-box” machine learning techniques, GE offers transparent human-readable rules, enabling firefighters and authorities to make rapid informed decisions. Future advancements could integrate real-time data (IoT sensors and satellites) and extend the methodology to other natural disasters. Protecting urban centers from fires is not only a technological challenge but also a moral imperative to safeguard human lives and societal cohesion. Full article

This research determines how to integrate factors related to evacuation in emergency preparedness using techniques for Multicriteria Decision-Making (MCDM). A distinctive MCDM technique that incorporates human behavior into evacuation models enhances decision-making and safety during emergencies, especially in vulnerable populations. For this purpose, a hybrid combination of MCDM methods—CRiteria Importance Through Intercriteria Correlation (CRITIC), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and Weighted Aggregated Sum Product Assessment (WASPAS)—is used to rank the vulnerability of Chilean regions by considering various factors. First, the related factors are ranked by CRITIC, and the result is that the “psychosocial problem” factor has the highest priority and weight. Then, according to the hybrid methods and CRITIC, all regions of Chile are ranked first with TOPSIS, WASPAS, and a combination of them to determine which one has the highest priority. The results show that the Santiago Metropolitan Region has the highest priority for vulnerability in all three methods. Full article